Given the word sequence of the problem text \(T\) , the text parser needs to translate it into a set of literals \(L_t\) , a sequence composed of predicates and variables. Recently, deep neural networks have achieved promising performances in sequence-to-sequence (Seq2Seq) learning tasks like machine translation [1]}, [2]}, [3]}. However, semantic parsers using Seq2Seq learning methods are not feasible to generate satisfactory literals in the Geometry3K dataset for two reasons. Firstly, the limited scale of geometry datasets weakens these highly data-driven methods. Secondly, neural semantic parsers tend to bring noises in generated results while geometry solvers with symbolic reasoning are sensitive to such deviations.
